EOD-EMDA: A method for automatically identifying and tracking individual pigs in herds by Wei Li, Hu Yuan, Xianpeng Zhu, Xiaojiang Yang

“…In tracking tasks, EOD+EMDA outperforms five popular algorithms (SORT, DeepSORT, ByteTrack, StrongSORT, and OC-SORT), achieving superior results in IDs, IDF1, IDP, IDR, and MOTA.Field tests demonstrate that the proposed framework enables accurate, stable, and real-time tracking of pigs from oblique perspectives. …”
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IoT-Enhanced Smart Parking Management With IncepDenseMobileNet for Improved Classification by Xiaoxia Zheng, Wenxi Feng, Ning Wang, Huhemandula

“…This paper presents an innovative framework for improving parking operations, dynamic pricing, and resource allocation in urban environments.…”
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Photon–photon chemical thermodynamics of frequency conversion processes in highly multimode systems by Huizhong Ren, Georgios G. Pyrialakos, Qi Zhong, Fan O. Wu, Mercedeh Khajavikhan, Demetrios N. Christodoulides

“…By utilizing fundamental notions from optical statistical mechanics, we develop a universal theoretical framework that effectively treats all frequency components as chemical reactants/products, capable of undergoing optical thermodynamic reactions facilitated by a variety of multi-wave mixing effects. …”
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Physics-Guided Self-Supervised Learning Full Waveform Inversion with Pretraining on Simultaneous Source by Qiqi Zheng, Meng Li, Bangyu Wu

“…The inversion network is a partial convolution attention modified UNet (PCAMUNet), which combines local feature extraction with global information integration to achieve high-resolution velocity model estimation from seismic shot gathers. …”
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ST-MSRN: An enhanced spatio-temporal super-resolution model for complex meteorological data reconstruction by Ping Mei, Zhi Yang, Changzheng Liu, Lei Wang, Zixin Yin

“…To address these limitations, this study proposes a Spatio-Temporal Multi-Scale Residual Network (ST-MSRN), which integrates a Multi-Scale Residual Feature Block (MSRFB) with a Channel Stacking Mechanism. The framework employs parallel multi-scale convolutions to hierarchically extract meteorological patterns, while the integrated Efficient Multi-scale Attention (EMA) module adaptively weights features based on spatio-temporal heterogeneity. …”
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Kinematic Integration Network With Enhanced Temporal Intelligence and Quality-Driven Attention for Precise Joint Angle Prediction in Exoskeleton-Based Gait Analysis by Lyes Saad Saoud, Irfan Hussain

“…This study introduces KINETIQA (Kinematic Integration Network with Enhanced Temporal Intelligence and Quality-Driven Attention), a novel predictive framework that advances the state of the art in knee angle prediction, demonstrating significant improvements in accuracy and clinical utility. …”
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A novel image fusion method based on UAV and Sentinel-2 for environmental monitoring by Fan Zhang, Aobo Guo, Zhenqi Hu, Yusheng Liang

“…Results demonstrate that the stacked learning model, combined with cubic convolution resampling, reduces the MAPE of NDVI values between Sentinel-2 and UAV imagery from 54.31 to 10.01%, markedly improving accuracy. …”
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DCCPNet: A Dual-Branch Channel Cross-Concatenation Pan-Sharpening Network for Satellite Remote Sensing Imagery by Zechun Li, Xunqiang Gong, Ailong Ma, Haiqing He, Pengyuan Lv, Xiansan Liu, Yanfei Zhong

“…., DCCPNet, is proposed, which introduces DCC blocks to facilitate bidirectional feature exchange between panchromatic and multispectral branches. Within this framework, hierarchical features are extracted through multiscale convolution residual (MCR) blocks, while complementary spatial and spectral information is adaptively fused in the DCC blocks. …”
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An enhanced network for extracting tunnel lining defects using transformer encoder and aggregate decoder by Bo Guo, Zhihai Huang, Haitao Luo, Perpetual Hope Akwensi, Ruisheng Wang, Bo Huang, Tsz Nam Chan

“…We propose a deep network model utilizing an encoder–decoder framework that integrates Transformer and convolution for comprehensive defect extraction. …”
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An Intelligent Field Monitoring System Based on Enhanced YOLO-RMD Architecture for Real-Time Rice Pest Detection and Management by Jiangdong Yin, Jun Zhu, Gang Chen, Lihua Jiang, Huanhuan Zhan, Haidong Deng, Yongbing Long, Yubin Lan, Binfang Wu, Haitao Xu

“…This integrated solution addresses the dual requirements of precision and timeliness in field monitoring, representing a significant advancement for agricultural vision systems. The developed framework provides practical implementation pathways for precision pest management under real-world farming conditions.…”
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MSA-Net: a multi-scale and adversarial learning network for segmenting bone metastases in low-resolution SPECT imaging by Yusheng Wu, Qiang Lin, Yang He, XianWu Zeng, Yongchun Cao, ZhengXing Man, Caihong Liu, Yusheng Hao, Zhengqi Cai, Jinshui Ji, Xiaodi Huang

“…Methods We propose a deep learning-based segmentation framework that integrates conditional adversarial learning with a multi-scale feature extraction generator. …”
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YOLOv8-RBean: Runner Bean Leaf Disease Detection Model Based on YOLOv8 by Hongbing Chen, Haoting Zhai, Jinghuan Hu, Hongrui Chen, Changji Wen, Yizhe Feng, Kun Wang, Zhipeng Li, Guangyao Wang

“…To address this issue, this study proposes an improved detection model, YOLOv8_RBean, based on the YOLOv8n object detection framework, specifically designed for runner bean leaf disease detection. …”
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EDT-Net: A Lightweight Tunnel Water Leakage Detection Network Based on LiDAR Point Clouds Intensity Images by Zhenyu Liu, Xianjun Gao, Yuanwei Yang, Lei Xu, Shaoning Wang, Ningsheng Chen, Zhiwei Wang, Yuan Kou

“…Integrating efficient receptive field expansion convolution into lightweight network models facilitated efficient feature extraction. …”
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Polara-Keras2c: Supporting Vectorized AI Models on RISC-V Edge Devices by Nizar El Zarif, Mohammadhossein Askari Hemmat, Theo Dupuis, Jean-Pierre David, Yvon Savaria

“…While traditional AI frameworks are powerful, they often fall short in meeting the requirements of edge computing, such as low latency, constrained computational power, and energy efficiency. …”
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Efficient and Effective NDVI Time-Series Reconstruction by Combining Deep Learning and Tensor Completion by Ang Li, Menghui Jiang, Dong Chu, Xiaobin Guan, Jie Li, Huanfeng Shen

“…The experiments conducted on moderate resolution imaging spectroradiometer NDVI data show that the NIT-Net framework is superior to most of the comparison methods. …”
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PC3D-YOLO: An Enhanced Multi-Scale Network for Crack Detection in Precast Concrete Components by Zichun Kang, Kedi Gu, Andrew Yin Hu, Haonan Du, Qingyang Gu, Yang Jiang, Wenxia Gan

“…To address these limitations, we propose PC3D-YOLO, an enhanced framework derived from YOLOv11, which strengthens long-range dependency modeling through multi-scale feature integration, offering a novel approach for crack detection in precast concrete structures. …”
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SFDA-MEF: An Unsupervised Spacecraft Feature Deformable Alignment Network for Multi-Exposure Image Fusion by Qianwen Xiong, Xiaoyuan Ren, Huanyu Yin, Libing Jiang, Canyu Wang, Zhuang Wang

“…Therefore, we propose an unsupervised learning framework for the multi-exposure fusion of optical spacecraft image sequences. …”
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Surface Defect and Malformation Characteristics Detection for Fresh Sweet Cherries Based on YOLOv8-DCPF Method by Yilin Liu, Xiang Han, Longlong Ren, Wei Ma, Baoyou Liu, Changrong Sheng, Yuepeng Song, Qingda Li

“…To address the challenges of rapid and accurate defect detection in intelligent cherry sorting systems, this study proposes an enhanced YOLOv8n-based framework for sweet cherry defect identification. First, the dilation-wise residual (DWR) module replaces the conventional C2f structure, allowing for the adaptive capture of both local and global features through multi-scale convolution. …”
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TCN–Transformer Spatio-Temporal Feature Decoupling and Dynamic Kernel Density Estimation for Gas Concentration Fluctuation Warning by Yanping Wang, Longcheng Zhang, Zhenguo Yan, Jun Deng, Yuxin Huang, Zhixin Qin, Yuqi Cao, Yiyang Wang

“…A flood optimization algorithm (FLA) is used to establish a hyperparameter collaborative optimization framework. Compared to TCN-LSTM, CNN-GRU, and other hybrid models, the hybrid model proposed in this study exhibits superior point prediction performance, with a maximum R² of 0.980988. …”
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Research Advances in Underground Bamboo Shoot Detection Methods by Wen Li, Qiong Shao, Fan Guo, Fangyuan Bian, Huimin Yang

“…To address these, an integrated intelligent framework is proposed: (1) three-dimensional modeling via multi-sensor fusion for subsurface mapping; (2) artificial intelligence (AI)-driven harvesting robots with adaptive excavation arms and obstacle avoidance; (3) standardized cultivation systems to optimize soil conditions; (4) convolution neural network–transformer hybrid models for visual-aided radar image analysis; and (5) aeroponic AI systems for controlled growth monitoring. …”
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